PaCTS 1.0: A Crowdsourced Reporting Standard

for Paleoclimate Data

D. Khider

1,2

, J. Emile‐Geay

, N. P. McKay

, Y. Gil

, D. Garijo

, V. Ratnakar

M. Alonso‐Garcia

, S. Bertrand

, O. Bothe

, P. Brewer

, A. Bunn

, M. Chevalier

L. Comas‐Bru

10,11

, A. Csank

, E. Dassié

, K. DeLong

, T. Felis

, P. Francus

A. Frappier

, W. Gray

, S. Goring

, L. Jonkers

, M. Kahle

, D. Kaufman

N. M. Kehrwald

, B. Martrat

22,23

, H. McGregor

, J. Richey

, A. Schmittner

N. Scroxton

, E. Sutherland

, K. Thirumalai

, K. Allen

, F. Arnaud

, Y. Axford

T. Barrows

, L. Bazin

, S. E. Pilaar Birch

, E. Bradley

, J. Bregy

, E. Capron

O. Cartapanis

,H.‐W. Chiang

, K. M. Cobb

, M. Debret

, R. Dommain

J. Du

, K. Dyez

, S. Emerick

, M. P. Erb

, G. Falster

, W. Finsinger

D. Fortier

, Nicolas Gauthier

, S. George

, E. Grimm

, J. Hertzberg

F. Hibbert

, A. Hillman

, W. Hobbs

, M. Huber

, A. L. C. Hughes

55,56

S. Jaccard

, J. Ruan

, M. Kienast

, B. Konecky

, G. Le Roux

, V. Lyubchich

V. F. Novello

, L. Olaka

, J. W. Partin

, C. Pearce

, S. J. Phipps

, C. Pignol

N. Piotrowska

,M.‐S. Poli

, A. Prokopenko

, F. Schwanck

, C. Stepanek

G. E. A. Swann

, R. Telford

, E. Thomas

, Z. Thomas

, S. Truebe

L. von Gunten

, A. Waite

, N. Weitzel

, B. Wilhelm

, J. Williams

M. Winstrup

, N. Zhao

, and Y. Zhou

Information Sciences Institute, University of Southern California, Los Angeles, CA, USA,

Department of Earth Sciences,

University of Southern California, Los Angeles, CA, USA,

School of Earth and Sustainability, Northern Arizona

University, Flagstaff, AZ, USA,

Department of Geology, University of Salamanca, Salamanca, Spain,

Renard Centre of

Marine Geology, Ghent University, Ghent, Belgium,

Helmholtz‐Zentrum Geesthacht, Geesthacht, Germany,

Laboratory

of Tree‐Ring Research, Tuscon, AZ, USA,

Western Washington University, Bellingham, WA, USA,

University of

Lausanne, Lausanne, Switzerland,

School of Earth Sciences, University of College Dublin, Belﬁed, Ireland,

School of

Archaeology, Geography and Environmental Sciences, Reading University, Reading, UK,

Department of Geography,

University of Nevada, Reno, NV, USA,

CNRS, Bordeaux University, Bordeaux, France,

Louisiana State University,

Baton Rouge, LA, USA,

MARUM‐Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany,

Institut National de la Recherche Scientiﬁque, Quebec City, Québec, Canada, Geosiences, Skidmore College, Saratoga

Springs, NY, USA,

Laboratoire des Sciences du Climat et de l'Environnement (LSCE/IPSL), Gif‐sur‐Yvette, France,

Department of Geography, Univerisity of Wisconsin‐Madison, Madison, WI, USA,

Physical Geography, University

Freiburg, Freiburg, Germany, Geosciences and Environmental Change Science Center, U.S. Geological Survey, Denver,

CO, USA,

Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research,

Spanish Council for Scientiﬁc Research, Barcelona, Spain,

Department of Earth Sciences, University of Cambridge,

Cambridge, UK,

School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, New South

Wales, Australia,

U.S. Geological Survey, St. Petersburg, FL, USA,

College of Earth, Ocean, and Atmospheric Sciences,

Oregon State University, Corvallis, OR, USA,

School of Earth Sciences, University College Dublin, Dublin, Ireland,

Rocky Mountain Research Station, U.S. Forest Service, Jemez Pueblo, NM, USA,

Department of Geosciences,

University of Arizona, Tucson, AZ, USA,

Department of Forest and Ecosystem Science, University of Melbourne,

Richmond, Victoria, Australia,

EDYTEM, Université Grenoble Alpes, University Savoie Mt Blanc, CNRS, Chambery,

France,

Department of Earth and Planetary Sciences, Northwestern University, Evanston, IL, USA,

Department of

Geography, University of Georgia, Athens, GA, USA,

Department of Computer Science, University of Colorado, Boulder,

Boulder, CO, USA,

Department of Geography, Indiana University Bloomington, Bloomington, IN, USA,

Physics of Ice,

Climate and Earth, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark,

Institute of Geological

Sciences, University of Bern, Bern, Switzerland,

Department of Geosciences, National Taiwan University, Taipei City,

Taiwan,

School of Earth and Atmospheric Sciences, Georgia Tech, Atlanta, GA, USA,

Université de Rouen Normandie,

Mont‐Saint‐Aignan, France,

Institute of Geosciences, University of Potsdam, Potsdam, Germany,

Earth and

Environmental Sciences, University of Michigan, Ann Arbor, MI, USA,

Instituto de Geociências, Laboratório de Sistemas

Cársticos, Universidade de São Paulo, São Paulo, Brazil,

The University of Adelaide, Adelaide, South Australia, Australia,

ISEM, CNRS, University Montpellier, Montpellier, France,

Département de Géographie, Université de Montréal,

Montréal, Québec, Canada,

Shcool of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA,

National Center for Atmospheric Science (NCAS), Department of Meteorology, University of Reading, Reading, UK,

Department of Earth Sciences, University of Minnesota, Minneapolis, MN, USA,

Department of Ocean, Earth, and

FEATURE ARTICLE

10.1029/2019PA003632

Key Points:

• First version of a crowdsourced

reporting standard for paleoclimate

data

• The standards arose through

collective discussions, both in person

and online, and via an innovative

social platform

• The standard helps meet the

interoperability and reuse criteria of

FAIR (Findable, Accessible,

Interoperable, and Reusable)

Supporting Information:

• Supporting Information S1

• Data Set S1

Correspondence to:

D. Khider,

khider@usc.edu

Citation:

Khider, D., Emile‐Geay, J.,

McKay, N. P., Gil, Y., Garijo, D.,

Ratnakar, V., et al. (2019). PaCTS 1.0: A

crowdsourced reporting standard for

paleoclimate data. Paleoceanography

and Paleoclimatology, 34,

Received 29 MAY 2019

Accepted 13 AUG 2019

Accepted article online 3 SEP 2019

KHIDER ET AL.

Williams

J. J.

Published online 201924 OC T

Corrected 21 FEB 2020

This article was corrected on 21 FEB

2020. See the end of the full text for

details.

1570

1 –1 .

https://doi.org/10.1029/2019PA003632

570 596

Atmospheric Sciences, Old Dominion University, Norfolk, VA, USA,

Research School of Earth Sciences, The Australian

National University, Canberra, ACT, Australia,

School of Geosciences, University of Louisiana at Lafayette, Lafayette,

LA, USA,

Antarctic Climate and Ecosystems Cooperative Research Center, University of Tasmania, Hobart, Tasmania,

Australia,

Earth, Atmospheric, and Planetary Sciences Department, Purdue University, West Lafayette, IN, USA,

Department of Geography, School of Environment, Education, and Development, University of Manchester,

Manchester, UK,

Department of Earth Science, University of Bergen and Bjerknes Centre for Climate Research, Bergen,

Norway,

School of Earth Sciences and Engineering, Sun Yat‐sen University, Guangzhou, China,

Department of

Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada,

Earth and Planetary Sciences, Washington

University, St. Louis, MO, USA,

EcoLab UMR 5245 CNRS‐Université de Toulouse, Toulouse, France,

Center for

Environmental Science, University of Maryland, Cambridge, MD, USA,

Geology Department, University of Nairobi,

Nairobi, Kenya,

Institute for Geophysics, The University of Texas at Austin, Austin, TX, USA,

Department of

Geoscience, Aarhus University, Aarhus, Denmark,

Institue for Marine and Antarctic Studies, University of Tasmania,

Hobart, Tasmania, Australia,

Institute of Physics‐CSE, Silesian University of Technology, Gliwice, Poland,

Department

of Geography and Geology, Eastern Michigan University, Ypsilanti, MI, USA,

Institut für Geologie und Mineralogie,

University of Cologne, Cologne, Germany,

Centro Polar e Climatico, UFRGS, Rio Grande do Sul, Brazil,

Alfred

Wegener Institute‐Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany,

School of Geography,

University of Nottingham, Nottingham, UK,

Department of Biological Sciences, Bergen University, Bergen, Norway,

British Antarctic Survey, Cambridge, UK,

School of Biological, Earth, and Environmental Science, UNSW, Sydney,

New South Wales, Australia,

Arizona State Parks and Trails, Benson, AZ, USA,

PAGES International Project Ofﬁce,

Bern, Switzerland,

ANGARI Foundation, West Palm Beach, FL, USA,

Institute of Environmental Physics, Heidelberg

University, Heidelberg, Germany,

Université Grenoble Alpes, CNRS, IRD, Grenoble, INP, IGE, Grenoble, France,

Department of Geography, University of Wisconsin Madison, Madison, WI, USA,

Department of Social Sciences,

82 83

Abstract The progress of science is tied to the standardization of measurements, instruments, and data.

This is especially true in the Big Data age, where analyzing large data volumes critically hinges on the data

being standardized. Accordingly, the lack of community‐sanctioned data standards in paleoclimatology

has largely precluded the beneﬁts of Big Data advances in the ﬁeld. Building upon recent efforts to

standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate

Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures

which information should be included when reporting paleoclimate data, with the goal of maximizing the

reuse value of paleoclimate data sets, particularly for synthesis work and comparison to climate model

simulations. Initiated by the LinkedEarth project, the process to elicit a reporting standard involved an

international workshop in 2016, various forms of digital community engagement over the next few years,

and grassroots working groups. Participants in this process identiﬁed important properties across

paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identiﬁed

archive‐speciﬁc properties and distinguished reporting standards for new versus legacy data sets. This work

shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata

accompanying paleoclimate data sets. Since such goals are at odds with present practices, we discuss a

transparent path toward implementing or revising these recommendations in the near future, using both

bottom‐up and top‐down approaches.

Plain Language Summary Standardizing the way data are described and shared is key to

accelerating the progress of science. Building on recent advances in paleoceanography and

paleoclimatology, we present the ﬁrst community‐led reporting standard for such datasets. The Paleoclimate

Community reporTing Standard (PaCTS) provides guidelines as to which information should be included

when reporting data from various paleoclimate archives, as well as themes common to many ﬁelds, like

uncertainty and other site‐speciﬁc information. The ultimate goal of this effort is to (1) make these datasets

more re‐usable over the long term, and (2) provide a roadmap for implementing and revising the standard,

as the ﬁeld of paleoclimatology and its practitioners both evolve. The requirements are driven by the

differing needs of data producers and the data consumers, who often have different goals in mind. Thus,

agreeing on and writing up these requirements involves building consensus among the community to decide

on their present and future goals.

10.1029/2019PA003632

Paleoceanography and Paleoclimatology

KHIDER ET AL.

Oxford Brookes University, Oxford, UK, University of Copenhagen, Copenhagen, Denmark, Max Planck Institute for

Chemistry, Mainz, Germany, Lamont

‐

Doherty Earth Observatory, Columbia University, Palisades, NW, USA

1571

1. Introduction

Paleoclimatology is a highly integrative discipline, often requiring the comparison of multiple data sets

and model simulations to reach fundamental insights about the climate system. Currently, such

syntheses are hampered by the time and effort required to transform the data into a usable format

for each application. This task, called data wrangling, is estimated to consume up to 80% of researcher

time in some scienti ﬁc ﬁelds (Dasu & Johnson, 2003), an estimate commensurate with the experience

of many paleoclimatologists, particularly at the early‐career stage. Wrangling involves not only

identifying missing values or outliers in the time series but also searching multiple databases for

the scattered records, contacting the original investigators for the missing data and metadata, and

organizing the data into a machine‐readable format. Further, this wrangling requires an understanding

of each data set's originating ﬁeld and its unspoken practices and so cannot be easily automated or out-

sourced to unskilled labor or software. There is therefore an acute need for standardizing paleoclimate

data sets.

Indeed, standardization accelerates scientiﬁc progress, particularly in the era of Big Data, where data should

be Findable, Accessible, Interoperable, and Reusable (FAIR; Wilkinson et al., 2016). Standardization is

critical to many scientiﬁc endeavors: efﬁciently querying databases, analyzing the data and visualizing

the results; removing participation barriers for early‐careers scientists or people outside the ﬁeld; redu-

cing unintended errors in data management; and ensuring appropriate credit of the original authors.

While the paleoclimate community has made great strides in this direction (e.g., Williams et al., 2018),

much work remains. The recent adoption of the FAIR data principles (Wilkinson et al., 2016) by the

American Geophysical Union (Stall et al., 2017) elevates the urgency of deﬁning what data and metadata

should be archived, and how. This article proposes a community‐recommended set of preliminary report-

ing standards and an open platform to determine which metadata are important for public archival, with

an eye toward maximizing the long‐term value of hard‐earned paleoclimate observations and ensuring

optimal reuse.

The need for standardization in paleoclimate research is beyond vocabulary agreement. Consider the editor-

ial of Wolff (2007), which tackled the ambiguous deﬁnition of time in the paleoclimate community. The

notation before present (BP) has become a de facto standard in the community, although “present” means

different things to different people. It is often taken as Common Era (CE) 1950 (especially within the radio-

carbon community), undeﬁned, or deﬁned as some other date (e.g., CE 2000), or the year the study was per-

formed/published. For studies spanning several million years with age uncertainties in excess of 1,000 years,

a50‐year difference is immaterial. However, for studies working at higher resolution (e.g., decadal to sub-

annual), concentrating on recent millennia, this difference is consequential. Thus, an agreement over the

precise meaning of the term present turns out to be critical to many uses of these data sets. The same can

be said of many other metadata properties, underscoring the need for common practices in paleoclimate

data reporting.

Given this acute need for standardization, the National Science Foundation EarthCube‐funded LinkedEarth

project nucleated a discussion on data reporting practices. EarthCube (2015) deﬁnes a standard as “a public

speciﬁcation documenting some practice or technology that is adopted and used by a community.” The

emphasis on community and practice underlines the cooperative nature of standard development. If only

one person uses a technical speciﬁcation, it is not a standard. If it is voted on but not applied in practice, it

is of little practical use.

Standardization requires three distinct elements: (1) a standard format for the data, (2) a standard termi-

nology for metadata, and (3) standard guidelines for reporting data (i.e., reporting standards). We note

that some prior knowledge of standardization practices (e.g., which data to include) can be useful in

the planning stages of data collection. As an analogy, consider the organization of library cards into an

old‐fashioned ﬁle cabinet. For this system to function, one needs (1) a set of compartments and drawers

to house the information, (2) labels to identify and classify the contents of the drawers, and (3) a disci-

plined adherence to the classiﬁcation system. This entails including essential information required for

application and reuse of the cards and the information they contain. In other words, every user follows

similar guidelines to generate, use, and ﬁle the cards; otherwise, the classiﬁcation falls apart and the

cards may as well be stored in a random pile.

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This article focuses on the last requirement, namely, the creation of standards for reporting paleodata and

metadata. It builds upon recent efforts to address the ﬁrst two points. On the ﬁrst point, the Linked

PaleoData format (LiPD; McKay & Emile‐Geay, 2016) and derived vocabulary agreements to describe paleo-

climate data (the LinkedEarth Ontology; Emile‐Geay et al., 2019) provide a data container for paleoclimate

data (section 2), which is currently used in a range of data analysis software (Bradley et al., 2018; Khider

et al., 2018; McKay et al., 2018). On the second point, the National Oceanic and Atmospheric

Administration (NOAA) World Data Service for Paleoclimatology (WDS‐Paleo) has created a set of standard

names to document paleoclimate variables, the Paleoenvironmental Standard Terms (PaST) Thesaurus

(National Oceanographic and Atmospheric Administration, 2018).

This article's aim is twofold: First, to provide a snapshot of the ﬁrst version of the Paleoclimate Community

reporTing Standard (PaCTS), as of 2019, with the understanding that this standard will eventually evolve,

and second, to document the process of community elicitation of such guidelines, so as to provide maximum

transparency on why and how these decisions were made. We start from the premise that sampling decisions

predate these reporting decisions, so the standard aims to guide an investigator's decisions as to how they

should report existing measurements, for example, at the time of publication.

The remaining sections are organized as follows: Section 2 summarizes the relevant prior standardization

efforts, which serve as the foundation for PaCTS v1.0. Section 3 describes the standardization process, includ-

ing eliciting community feedback. Section 4 presents recommendation from a group of 135 international

researchers actively engaged in paleoclimate research. Section 5 illustrates the application of PaCTS v1.0

to an existing paleoclimate record. Finally, Section 6 concludes with a plan to disseminate the ﬁrst version

of PaCTS within the paleoclimate community and provides a roadmap for further standards development

and their future applications.

2. Background

2.1. The LinkedEarth Framework: An Online Approach to Standard Development

The LinkedEarth project established an online (Gil et al., 2017) that enables the curation of metadata for pub-

licly accessible data sets by experts and fosters the development of terminology agreements and standards for

paleoclimate metadata. Our approach builds on two synergistic elements: (1) the LinkedEarth Ontology

(Emile‐Geay et al., 2019), which provides an unambiguous structure and terminology to describe the meta-

data of a paleoclimate data set, and (2) the LinkedEarth Platform (Gil et al., 2017), which enables the colla-

borative authoring of highly structured metadata about paleoclimate data sets using the terms in the

LinkedEarth Ontology.

The LinkedEarth Ontology represents vocabulary agreements to describe paleoclimate metadata. In a

domain like paleoclimatology, we usually can distinguish the different kinds of objects that we want to

describe (i.e., a sample, a measurement, and a data set) and the relationships used to describe those objects

(e.g., a measurement is taken from a sample and therefore they are related and the measurement is in a data

set and therefore they are related). An ontology is a formal way to represent objects and their properties, and

they represent consensual knowledge that helps a community describe major concepts in the domain using

common terms. Speciﬁcally, an ontology formalism allows the representation of objects types as classes and

relationships as properties of those classes. Classes can have subclasses, and a given class can be a subclass of

several classes. For example, the class proxy archive can have coral as a subclass, and the class repository item

can have sample as a subclass. A feature of ontologies is that they allow the creation of machine‐readable

metadata, that is, data descriptions that can be queried programmatically by machines to retrieve data sets

of interest. Thanks to the ontology, machines can navigate through metadata and discover data that other-

wise would be hidden to them. LinkedEarth relies on semantic web technologies to represent ontologies, spe-

ciﬁcally the Web Ontology Language (OWL) standard of the World Wide Web Consortium (W3C; W3C OWL

Working Group, 2012). More details are provided in Emile‐Geay et al. (2019).

The LinkedEarth Platform allows users to (1) describe paleoclimate data sets using the terms available in the

LinkedEarth Ontology and (2) propose new terms if they cannot ﬁnd an appropriate one in the ontology. The

LinkedEarth Platform is a sociotechnical system, and as such, it provides technology infrastructure coupled

with social processes that support terminology and standards convergence. When users describe a

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1573

paleoclimate data set, the terms in the existing LinkedEarth Ontology are offered to them as editable forms

and completion commands, which promotes adoption. If a user does not ﬁnd a term that is appropriate for

their data set, they can create a new term on the ﬂy. Such new terms can then be discussed on the platform,

building community consensus on their deﬁnitions and the essential status of their inclusion to a data set.

The social extensions of the LinkedEarth Platform allow working groups to organize activities by users with

similar expertise to build a common vocabulary. Each working group was assigned a special page on the

LinkedEarth Platform to nucleate their activities, including discussions and polls for rapid community

feedback. The terms discussed within these working groups form the crowdsourced part of the

LinkedEarth Ontology. The social editorial processes eventually will lead to a new version of the

LinkedEarth Ontology. The LinkedEarth Platform and its associated social processes are described in detail

in Gil et al. (2017).

The LinkedEarth Platform is implemented as an extension of the Semantic MediaWiki framework (Krötzsch

& Vrandečić, 2011). Semantic wikis augment traditional wikis with the ability to structure information

through (1) semantic annotations, which enable the assignment of a class (or category) to an object in a wiki

page and properties (or qualiﬁers) that are useful to describe that object and (2) automated reasoning capabil-

ities that exploit those annotations to organize the wiki's knowledge (Gil, 2013). For example, if the page for

Los Angeles is annotated as being in the class city and having a property location = California, and the page

for California has a property that location = U.S.; then the semantic wiki can infer that Los Angeles is in the

U.S. even though that was not explicitly stated. Semantic wiki pages can also include queries that are exe-

cuted when the page is visited, so dynamic content is created in a way that is up to date with the latest addi-

tions. Semantic wikis also have facilities to track edits together with the data and contributor, so that the

provenance of edits can be examined and undesirable ones can be easily undone. The content of semantic

wikis becomes part of the open Semantic Web, as it can be published as a set of linked Web objects in the

Web of Data, following Linked Data Principles (Heath & Bizer, 2011). With this approach, the metadata

for all paleoclimate data sets deﬁned in the wiki becomes openly available on the Web, machine readable,

and can be queried programmatically by any application. More details are provided in Gil et al. (2017).

2.2. Previous and Concurrent Efforts Toward a Data Standard

The discussion below is nonexhaustive and only focuses on the relevant efforts that have sparked the discus-

sion about PaCTS.

2.2.1. Origins of a Standard Format for Paleoclimate Data

Climate modeling has greatly beneﬁtted from the netCDF data format (Unidata, 2019), designed to support

the creation, access, and sharing of array‐oriented data, including climate model output. Despite the impor-

tance of paleoclimate data availability for model evaluation (Masson‐Delmotte et al., 2013), until recently,

there was no universal container to describe, store, and share these data sets. Emile‐Geay and Eshleman

(2013) ﬁrst introduced the idea of a ﬂexible container, where metadata would be stored semantically with

the numeric data in tabular form. This concept was the basis for the LiPD format (McKay & Emile‐

Geay, 2016).

LiPD is a universally readable data container that organizes paleoclimate data and metadata in a uniform

way. It is based on JSON‐LD (JavaScript Object Notation for Linked Data), a JSON‐based format compliant

with the Linked Data paradigm. JSON is a lightweight data interchange format that is easy for humans and

machines alike to read and write. LiPD has six distinct components: root metadata (e.g., data set name, inves-

tigator, and version); geographic metadata (e.g., coordinates and descriptive location such as a country or

city); publication metadata (e.g., authors, title, journal, and digital object identiﬁer [DOI]); funding metadata

(e.g., funding agency and grant number); PaleoData, which includes all the measured (e.g., Mg/Ca) and

inferred (e.g., sea surface temperature) paleoenvironmental data; and ChronData, which mirrors

PaleoData for information pertaining to age. These components provide the rigidity necessary to write robust

codes around the format while remaining extensible enough to capture (meta)data as rich as the users want

to provide for them. Utilities in Matlab, Python, and R (Heiser et al., 2018) allow users to interact with the

ﬁles (speciﬁcally, to read, write, query, or ﬁlter data sets matching speciﬁ ed conditions).

In many ways, LiPD is intended to be the netCDF of paleoclimate observational data. However, although

both LiPD and the LinkedEarth Ontology provide a standard way to describe a paleoclimate data set, they

say little about what information should be stored to ensure reuse. The endorsement of netCDF by a

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broad community further beneﬁted from the adoption of the Climate and Forecast (CF) conventions

(Gregory, 2003). The CF conventions deﬁne metadata describing what the data in each variable represents,

and the spatial and temporal properties of the data. In other words, it deﬁnes both a set of common terms

(a standard vocabulary) and a reporting standard. Efforts toward standardization of common terms have

been undertaken by WDS‐Paleo in the form of the PaST thesaurus (National Oceanographic and

Atmospheric Administration, 2018), which provides the preferred option for a standardized name and deﬁ-

nition. PaCTS details a crowdsourced approach for deciding what information should be included when

reporting paleoclimate data, a CF convention for paleoclimate data sets.

2.2.2. Archive‐Focused Initiatives

Attempts at paleoclimate data standardization have a long history. For data sets derived from wood archives,

LinkedEarth relied on the tree‐ring data standard, TRiDaS (Jansma et al., 2010), which complies with estab-

lished data standards such as Dublin Core (DCMI Usage Board, 2008). The TRiDaS project aimed at deﬁning

the properties that are used in the dendro community and give them a consistent name (i.e., a controlled

vocabulary) and identifying whether the quantity should be mandatory and repeatable (i.e., best practices).

These efforts help inform the PaCTS one for wood archives, though it should be noted that tree‐ring science

is far broader than dendroclimatology, involving applications to paleoﬁre, landscape evolution, paleoecol-

ogy, art history, and archeology. Because PaCTS is focused on paleoclimate, we reused the relevant subset

of the TRiDaS standard.

A discussion regarding paleoceanographic data standards was started during the Paleoclimate Model

Intercomparison Project (PMIP) Ocean Workshop 2013—Understanding Changes Since the Last Glacial

Maximum (hereafter, PMIP LGM) in Corvallis, Oregon, in December 2013. Given the expertise of the work-

ing group members, the discussion focused on marine sedimentary archives and was summarized into a

document, which is available on the LinkedEarth Platform (Kucera et al., 2013). Their recommendations

served as the foundation for a preliminary reporting standard for records based on marine sedimentary

archives. Although the group identiﬁed recommended properties to be included with marine data sets, they

did not propose a complete vocabulary nor a subset of required properties for acceptance in a database.

The Marine Annually Resolved Proxy Archives (MARPA) working group, nucleated under the EarthCube

umbrella, is one of the ﬁrst grassroots efforts within the paleoclimate community to enhance and facilitate

the archiving and sharing of paleoclimate data as they pertain to annually resolved archives (e.g., corals, mol-

lusks, coralline algae, and sclerosponges; Dassié et al., 2017). Their efforts included a registry of physical sam-

ples and their associated geochemical data and metadata, which are our primary focus here. The MARPA

group summarized their recommendations in a document that was circulated among the community and

constitutes the backbone of the recommendations presented here. Most of these recommendations were also

applicable to other archives, rather than MARPA‐speciﬁc, underscoring that despite their diversity, paleocli-

mate data sets retain common core properties that facilitate multiproxy syntheses and comparisons.

The Speleothem Isotopes Synthesis and Analysis (SISAL) group was formed under the international Past

Global Changes (PAGES) project and aimed at bringing together speleothem scientists, process modelers,

statisticians, and climate modelers to develop a global synthesis of speleothem isotopes that can be used to

further our understanding of past climate variability and in model evaluation. As part of this initiative, a tem-

plate was created, outlining the necessary metadata for speleothem‐based records (Atsawawaranunt et al.,

2018). This template (Comas‐Bru & Harrison, 2019) forms the backbone of properties applicable to spe-

leothems‐based records presented here.

2.3. Workshop on Paleoclimate Data Standards

The workshop on paleoclimate data standards held in Boulder, USA in June 2016 (Emile‐Geay & McKay,

2016, Figure 1) served as a focal point to initiate a broader process of community engagement and feedback

solicitation, with the goal of generating a community‐vetted standard for reporting paleoclimate data.

Workshop participants identiﬁed the necessity to distinguish a set of essential, recommended, and desired

properties for each data set. By default, any and all information was considered desired, though we shall

see exceptions to this principle. A subset of the archived information should be recommended to ensure opti-

mal reuse of the data set. Yet a smaller subset of this information is deﬁned as essential, meaning that the data

set cannot be reused reliably or at all without these critical pieces of information.

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A consensus emerged that these distinctions are archive‐speciﬁc; for instance, what is needed to meaningfully

reuse a speleothem record could be quite different from what is needed to meaningfully reuse an ice core

record. It was therefore decided that experts on particular paleoclimate archives organized into working

groups (WGs) would be best positioned to elaborate and discuss the components of a data standard for their

speciﬁc subﬁeld of paleoclimatology. Consequently, seven WGs were created on the LinkedEarth Platform

centered around the main archives used in paleoclimate studies: historical documents, ice cores, lake sedi-

ments, marine sediments, MARPA, speleothems, and tree rings. A call for additional WGs was made in the

fall of 2016. Observations common to two or more archives (e.g., alkenones) were discussed in one WG with

a link to the discussion in other WGs. It is also critical to ensure interoperability among standards to enable

investigations using multiple observations on the same archive and across archives; to that end, three long-

itudinal WGs were created to deal with information common to all archives (such as publication, geographi-

cal coordinates, and funding information), to report uncertainties in the record, and to report how

chronologies were established.

The workshop participants also identiﬁed the need to have a separate set of requirements for newly generated

data sets and legacy data sets, for which less metadata would likely be available. In PaCTS v1.0, a legacy data

set is deﬁned as a data set that is not being archived by the author(s) of the original study.

3. Toward PaCTS

3.1. Working Groups

Rules of engagement on the LinkedEarth Platform were published in the fall of 2016 along with the establish-

ment of seven WGs (ice cores, lake sediments, marine sediments, MARPA, speleothems, trees, and uncertain-

ties, Figure 1). Three WGs (chronologies, cross‐archive, and historical documents) followed in the spring of

2017 as additional archives, and common information to all archives were identiﬁed. Each WG leader was

tasked to organize their subcommunity either directly on the platform, through videoconferences, meetings

at conferences, and/or other working groups (e.g., MARPA group and the PAGES SISAL group). The WG lea-

ders were tasked to regularly update the discussion directly on the LinkedEarth platform or provide a docu-

ment for integration on the platform. One difﬁculty in deﬁning desired, essential, and recommended

properties was related to the expected use of the data: Depending on what one wants to do with the data,

one needs different metadata. By far, the most important and metadata‐hungry task is to perform queries

to ﬁnd data sets pertinent to a scientiﬁc question.

As an example of ﬁnding data sets pertinent to a scientiﬁc question, consider a study conducted by a paleo-

ceanographer who wants to characterize millennial‐scale sea surface temperature (SST) variability during

Figure 1. Timeline of the community elicitation for best practices in paleoclimate data reporting. The Workshop on

Paleoclimate Data Standard marks the ofﬁcial beginning of the endeavor. PaCTS collects responses from the

LinkedEarth platform, Twitter polls, and survey up to November 2017.

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the Holocene epoch (Khider et al., 2016). In the current research ecosystem, a typical workﬂow would consist

of querying several databases to ﬁnd suitable records, extract the data, consult the original publication(s) for

additional metadata (e.g., author's deﬁnition of present), reformat the data into a coherent format for analy-

sis, apply spectral analysis to examine the frequency content of the records, perform some statistical analysis

of the results, and visualize them. In an ideal world, the query, preferably from a single database, should (1)

ﬁnd records that span the Holocene, (2) ﬁnd the subset of those that primarily reﬂect SST, and (3) ﬁnd the

subset of that subset with a speciﬁed resolution (e.g., ﬁner than 200 years) to have at least ﬁve data points

per 1,000‐year cycle (a permissive assumption for this sort of work). Simple though it may seem, this query

requires the following (meta)data: (1) a measure of age (time) and minimum and maximum values of the

time series; (2) an estimate of SST, as an inferred variable, and/or Mg/Ca, U

k’

, TEX

, or microfossil assem-

blages as measured variables from which SST can be inferred; and (3) temporal resolution, calculated from

the data.

Other types of basic queries include: searching for a particular publication, using either the DOI, title, jour-

nal, or authors; and searching by the type of archives. Deﬁning the search parameters for these complex

queries on the LinkedEarth platform (Khider & Garijo, 2018) sparked the discussion for the

needed properties.

A standard helps not only with the menial task of searching for records in a database. Such a standard can

also assist with doing the science per se, by ensuring that the required information is present in the data

set. For instance, making a simple map of all the records in a database by archive types (Figure 1a of

PAGES2k Consortium, 2017) requires each data set to report latitude, longitude, and the archive type.

More complex data analysis requires more information: to investigate the effect of age uncertainties (e.g.,

with the Bchron (Haslett & Parnell, 2008) or BACON (Blaauw & Christen, 2011) packages, or to establish

new depth‐age models (Blois et al., 2011; Giesecke et al., 2014), one needs the raw radiocarbon measure-

ments, their measurement uncertainties, and associated depth in the archive.

3.2. Community Surveys

To decide which of the properties identiﬁed within the various WGs should be considered essential, recom-

mended, or desired, we ﬁrst gathered input via the LinkedEarth platform (Figure 2a). As of 1 August 2018, it

was home to 207 polls, with 796 votes given by 32 different users. On average, each question received three

votes, with some questions receiving no votes and others as many as 27. Note that some questions were dupli-

cated across different WGs and the ﬁnal count presented here takes into account all votes received on the

platform. The low number of votes can be partially attributed to the fact that voting was only possible after

authentication onto the platform, creating a barrier to widespread participation. To broaden community

involvement, the polls were then threaded on Twitter from the LinkedEarth account with voting allowed

over a 7‐day period (Figure 2b). The Twitter polls increased engagement (by a factor of 3 on average) and also

led to discussions that were then moved to the LinkedEarth platform for traceability of decisions.

Finally, by request from the community, the questions were summarized in a survey distributed to the paleo-

climate community through the ISOGEOCHEM, CLIMLIST, paleoclimate, and cryolist list‐servs, as well as

the PAGES e‐news, website, and social media. The survey contained 603 questions across all working groups

for which respondents were asked to determine whether each property is deemed essential, recommended,

or desired for new and legacy data sets, in addition to open‐ended questions and prompts for community

feedback. The survey was more comprehensive than the polls on the LinkedEarth platform or Twitter since

all questions were framed to allow for a response for legacy and new data sets. On the other hand, the

LinkedEarth platform also contains duplicate questions across various WGs (e.g., should depth be reported

as essential, recommended, and desired), polls aiming to deﬁne the scope of the data sets housed on

LinkedEarth (e.g., should the LinkedEarth platform only contain data sets that appear in peer‐reviewed pub-

lications?), and the operating deﬁnition of legacy versus new data sets that was then used in the survey.

Ninety‐ﬁve scientists participated in the survey. Each question on the survey received on average 54 answers.

Paleoclimatology is a multidisciplinary effort where researchers typically have expertise in one or more proxy

systems (e.g., different observations on the same archive, similar observations on different archives, or a mix

of different sensors, observations, and archives). Scientists are often led to compare their own data sets to

others obtained from proxy systems with which they are less familiar. Consequently, the metadata they

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need tend to differ based on their level of expertise (it is easier to ﬁll in the blanks in one's own area of

expertise). For instance, an ice core expert interested in comparing their deuterium record with a nearby

record of SST would most likely only require the age at each horizon and associated SST. On the other

hand, an expert on foraminiferal Mg/Ca‐based SST reconstruction may also need information about the

cleaning methodology or the number of individual foraminifera in the sample. To ensure that both needs

were represented, respondents were encouraged to complete the entire survey, rather than focus

exclusively on their own areas of expertise.

3.3. Survey Responses

The 95 survey responses were then combined with the Twitter and LinkedEarth platform poll answers

(Figures 3 and 4 and Supplementary Information). In total, 135 participants from North America (52%),

Europe (36%), Australia (5%), Asia (4%), South America (2%), and Africa (1%) were identiﬁed across the

Figure 2. Example of polls on (a) the LinkedEarth platform and (b) Twitter (@Linked_Earth).

Figure 3. Example of a survey question for a new data set. The histogram represents the number of votes on each platform

(orange: LinkedEarth, purple: Twitter, and green: Google survey). The pie chart represents the fraction of the votes for

essential (green), recommended (pink), and desired (blue).

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survey and LinkedEarth platform. Since voting on Twitter is anonymous, it is impossible to identify these

voters or establish whether they voted on other platforms. We are aware that some researchers may have

answered the same question several times on the various platforms. Since the number of survey answers

dwarfs the number of votes on Twitter and the LinkedEarth platform (Supplementary Information) and

Twitter does not track the user names associated with the votes, we did not attempt to correct for multiple

responses. Therefore, 135 contributors represent our best estimate for the number of total participants.

Most of the polls on Twitter and the LinkedEarth platform referenced legacy versus new data sets.

However, in the cases where the data set status was not speciﬁed, we assumed that the question referred

to a new data set only. Furthermore, if a question was repeated on various WGs (e.g., latitude and long-

itude), the number of votes were tallied and included in the total count for the cross‐archive metadata

reporting (see section 4.1). Responses on the survey, Twitter, and the LinkedEarth platform were given

equal weight.

For each of the properties, we identiﬁed respondents' recommendation for both new and legacy data sets as

the majority vote. We used mind maps to visually organize the hierarchical information, keeping the rela-

tionship intact (Figures 5) and mosaic plots to display the frequencies of the essential, recommended, and

desired categories for each working group (Figure 6). Overall, the community identiﬁed 208 properties

(69% of polled properties) as essential, 82 (27%) properties as recommended, and 12 (4%) as desired for

new data sets. For legacy data sets, fewer properties were deemed essential: 131 (44%) of polled properties

versus 136 properties (45%) were considered recommended and 34 properties (11%) were identiﬁed as

desired. This difference is not unexpected and highlights the fact that legacy data sets, although not as meta-

data‐rich as new data sets, are still valuable to the community (Figure 6).

4. PaCTS v1.0: Paleoclimate Community reporTing Standard

This section is based on the recommendations made in the various WGs, which were then subject to polling

through the LinkedEarth platform, Twitter, and the survey. We are aware that these recommendations may

be incomplete for some archives, a point discussed in section 6. A list of these properties, deﬁnitions, and

associated recommendations are available on the LinkedEarth platform.

Figure 4. Same as Figure 3 for a legacy data set.

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Figure 5. Mind map of the various properties identiﬁed by the WGs and associated vote. Colors represent the different WGs. Parentheses indicate a different report-

ing standard for legacy data sets when different from new data sets. Available online at https://coggle.it/diagram/WqMd49MJtB8DbqfH/t/community‐standards‐

for‐paleoclimate‐data‐and‐metadata.

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4.1. Cross‐Archive Metadata

Despite their diversity, paleoclimate records (and compilations thereof) share common metadata properties

such as contributors, geographical information (e.g., coordinates and site name), publication information

(e.g., authors, title, journal, and DOI), funding information, and general information about the paleoenvir-

onmental and chronology data (e.g., should the raw data be included?). In total, the community identiﬁed

54 properties applicable to all archives (Figures 5 and 7).

For new data sets, 36 of these properties were identiﬁed as essential, 9 as recommended and 9 as desired. It is

not surprising that 67% of the properties were voted as essential since these properties are critical for the data

reuse with no expert knowledge about the proxy systems or paleoclimate. Likewise, 24 of these properties

(44%) were identiﬁed as essential for legacy data sets. For a data set to be reused, information regarding

the location, publication, and interpreted chronology and paleoenvironmental variables is critical. Hence,

several researchers commented that new data sets should contain both the raw and interpreted data. The

bar for legacy data sets should be lower, recognizing that much of the desired data may no longer be available

and that interpreted data are still useful for many applications.

In addition to the properties identi ﬁed, a data set DOI and a data set license would also promote data reuse.

LinkedEarth is not set up to mint DOIs directly, but they can be obtained through other platforms such as

PANGAEA, Dryad, or FigShare. The registry of research data repositories, re3data, gives information on

whether a repository provides persistent identiﬁers. The Creative Commons (CC‐BY) license is recom-

mended for paleoclimate data since under this license, other researchers are free to share and adapt materials

while giving appropriate credit to the original contributor of the resource.

Figure 6. Mosaic plots for (a) new data sets and (b) legacy data sets showing the number of essential, recommended,

and desired metadata for the various WGs. The height of the bar represents the fraction of total occurrences for

essential (e), recommended (r), and desired (d) votes, while the width of the bar represents the number of properties

voted on in each WG.

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4.2. Archive‐Speciﬁc Metadata

4.2.1. Ice Cores

The ice core WG identiﬁed 16 properties speciﬁc to glacier ice, including information pertaining to the

archive, such as melt in transport, storage conditions, the observations available for the archive, and the

chronology. For new data sets, eight properties were deemed essential and eight recommended. The number

of essential properties dropped to four for legacy data sets with three properties deemed recommended

(Figures 5, 6, and 8).

As with historical documents, most survey respondents were not experts on records generated on ice cores

and therefore only responded for properties they were likely to use.

4.2.2. Lake Sediments

The lake sediments WG reported 54 properties speciﬁc to this archive, which were grouped by proxy sensor/

observation types: particle size, mineralogy, imagery data, accumulation rate, and compound speciﬁc iso-

topes. Whereas some properties were common across the various types of observations (i.e., units, interpre-

tation, and pretreatment methods), many were observation‐speciﬁc (e.g., source of compound for

compound‐speciﬁc isotopes), highlighting the necessity of detailed sets of guidelines down to the proxy

observation level to meet researchers' needs.

Figure 7. Mind map of the various properties identiﬁed by the cross‐archive WG and associated vote. Color is the same as in Figure 5. Parentheses indicate recom-

mendations for legacy data sets when different from new data sets. Available online at https://coggle.it/diagram/W4W9podcxp86PPvf/t/cross‐archive‐metadata.

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For new data sets, 39 properties were identiﬁed as essential and 15 as recommended. For legacy data sets, 25

were seen as essential, 28 as recommended, and 1 as desired (Figures 5, 6, and 9). In addition to these 54

properties, the WG started a discussion on how to best report the concept of depth in the archive.

Although several WGs identiﬁed depth (i.e., position in the archive sample) as an essential property, espe-

cially for new data sets, none had deﬁned how this depth should be reported. The majority of the respondents

indicated a preference to report top and bottom depth for both new and legacy data sets although several

respondents proposed to lower the bar for legacy data sets to whatever is available for these records.

Respondents also noted that pictures of the core after the sampling process would be useful. Whether these

pictures should be available with the data or stored in the database of the physical sample repository is a deci-

sion best left to individual researchers, based on their constraints and mandates by funding entities.

4.2.3. Marine Sediments

The marine sediments WG identiﬁed 48 properties speciﬁc to this type of archives. These properties were

divided into six groups, according to the type of observation: general sampling, bulk sediment geochemistry,

foraminifera geochemistry, alkenones, the glycerol dialkyl glycerol tetraether (GDGT) proxies, and micropa-

leontology. The foraminifera geochemistry category was further subdivided into stable isotopes, boron iso-

topes, and trace elements. Although some of the requirements were common to all observations, this WG

included several observation‐speciﬁc properties such as the cleaning methodology for foraminiferal trace ele-

ments or raw peak areas for GDGTs.

For new data sets, 36 properties were identiﬁed as essential and 12 as recommended. The number of essential

properties drops to 24 for legacy data sets, with the remainder considered recommended (Figures 5, 6,

and 10).

4.2.4. Coral, Mollusks, and Other Annually Resolved Marine Records

The properties for these archives were taken from the spreadsheet the MARPA group had circulated online

for feedback. Most of these properties were applicable to all archives reporting geochemical properties and

were therefore incorporated into the cross‐archive WG and questions. Two archive‐speciﬁc properties were

also identiﬁed: interpolated chronologies (i.e., distance from core top translated to time, usually a calendar

Figure 8. Mind map of the various properties identiﬁed by the ice core archives WG and associated vote. Color is the same

as in Figure 5. Parentheses indicate recommendations for legacy data sets when different from new data sets. Available

online at https://coggle.it/diagram/W4XNNeGhIngfjHzB/t/historical‐documents.

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day for each sample then interpolated to even monthly intervals) and X‐ray pictures (and associated drilling

path). For both new and legacy data sets, the raw (distance from core top), interpolated chronologies, and

X‐ray pictures were considered essential and recommended, respectively (Figures 5 and 6). The reporting

of growth increments in mollusks and corals is still an ongoing discussion within MARPA.

4.2.5. Speleothems

When constructing their database (Atsawawaranunt et al., 2018), the SISAL WG identiﬁed 23 properties spe-

ciﬁc to speleothem records. The SISAL database only focuses on stable isotopes in speleothems, and these

properties only apply to this proxy system. These properties can be further subdivided into four categories

describing the cave and modern cave conditions, the physical sample, and information about the sample

data. For new data sets, 11 properties were considered essential and 12 recommended. For legacy data sets,

only 2 properties were considered essential and 21 were marked as recommended (Figures 5, 6, and 11).

Although evidence for equilibrium (e.g., the Hendy test; Hendy, 1971, or monitoring data that supports

equilibrium precipitation of calcite) was narrowly voted as essential for new data sets and recommended

for legacy data sets, three respondents (two on Twitter and one on the survey) expressed concerns about

the value of this property as it rarely shows up in monitoring data and the Hendy test has been abused

by the paleoclimate community. This illustrates the need for an evolving standard, one that ﬁts the needs

of the community and changes as our scientiﬁc understanding about proxy systems increases.

4.2.6. Tree‐Based Records

The tree ring community has a long history of developing and adopting data standards; however, the meta-

data capacity or requirements in earlier data formats (e.g., Tucson, Heidelberg, Shefﬁeld, CATRAS, and

Belfast among many others) were limited by the technology of the decade in which they were created

(Brewer et al., 2011). The 35 properties in the survey were taken from TRiDaS (Jansma et al., 2010) and

Figure 9. Mind map of the various properties identiﬁed by the lake sediments archives WG and associated vote. Color is the same as in Figure 5. Parentheses indi-

cate recommendations for legacy data sets when different from new data sets. Available online at https://coggle.it/diagram/W4h9m‐GhIjjbm3yX/t/lake‐sediments.

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Figure 10. Mind map of the various properties identiﬁed by the marine sediments archives WG and associated vote. Color is the same as in Figure 5. Parentheses

indicate recommendations for legacy data sets when different from new data sets. Available online at https://coggle.it/diagram/W4iIkodcxlDKTK6v/t/marine‐

sediments.

Figure 11. Mind map of the various properties identiﬁed by the speleothem archives WG and associated vote. Color is the same as in Figure 5. Parentheses indicate

recommendations for legacy data sets when different from new data sets. Available online at https://coggle.it/diagram/W4gwj‐GhIl4VmfYP/t/speleothem.

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from the proposed tree‐ring isotope databank (Csank, 2009). TRiDaS was chosen as a starting point as it was

designed as a standard to represent dendrochronological data across its many subdisciplines, including

dendroclimatology. TRiDaS therefore includes many (optional) properties as essential or recommended

that are not applicable to data sets collected for paleoclimate reconstructions.

For new data sets, 26 properties were considered essential, 7 recommended, and 2 desired. For legacy data

sets, 19 properties were voted on as essential, 9 as recommended, and 7 as desired (Figures 5, 6, and 12).

Several researchers were confused about the terms used in TRiDaS, suggesting that the standard may be

too broad for most paleoclimate applications and should be further reﬁned if it is to be widely adopted.

The reason for this confusion may be because TRiDaS was initiated by the cultural dendrochronology com-

munity (e.g., dendroarcheology, art, and building history) in a response to the more pressing need for stan-

dardized metadata in these disciplines. Despite attempts to engage all subdisciplines of dendrochronology in

the development of TRiDaS, the cultural aspects of the standard were more fully implemented due to the

greater participation of users from these areas of research.

Nevertheless, a subset of the ﬁelds deﬁned in TRiDaS was used as a starting point for discussion for PaCTS

v1.0. Many ﬁelds within TRiDaS are already addressed in the cross‐archive metadata and were disregarded,

leaving only dendro‐speciﬁc ﬁelds. These were then supplemented by ﬁelds for tree‐ring isotope data taken

from the tree‐ring isotope databank proposed by Csank (2009). Regretfully, discussion of the suitability of

these ﬁelds among the dendroclimatology community has been limited and the list of initial ﬁelds was not

subsequently reﬁned. The public voting process has resulted in a number of ﬁelds being marked as essential

that are not routinely (if ever) collected for dendroclimatological research. Furthermore, some of the quan-

tities that are being proposed are difﬁcult to measure or know, raising the issue of whether these properties

are even desired. Some of the properties are a characteristic of the data themselves (ring count) and not meta-

data per se. These may be useful as convenience ﬁelds when querying large data collections (rather than hav-

ing to extract and calculate).

The confusion in the voting process could re ﬂ ect confusion over whether PaCTS v1.0 is to be a data standard

applicable to all dendrochronological data sets or exclusively to those collected for use in climate

Figure 12. Mind map of the various properties identiﬁed by tree‐based archives WG and associated vote. Color is the same as in Figure 5. Parentheses indicate

recommendations for legacy data sets when different from new data sets. Available online at https://coggle.it/diagram/W4huaYdcxhdzTB9z/t/trees.

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reconstructions, for which a smaller number of essential ﬁelds would be required. It could also reﬂect

sampling bias in the voting process related to the composition of the WG.

While the work described here is clearly an important step towards incorporating dendroclimatological data

into a universally applicable paleoclimate data standard, there remains a great deal of work to be done. This

work needs to begin with discussions that engage a much broader cross section of the dendroclimatological

community and reﬁned criteria in subsequent surveys.

4.2.7. Documentary Archives

Historical documents differ quite signiﬁcantly from the other archive types presented in PaCTS v1.0.

Documentary data are extracted from written sources (books, chronicles, newspaper, etc.), and each of these

sources in the data set needs a reference to the publication metadata (in addition to the scientiﬁc publication

of the data in a journal). The raw data most comparable to measurements on other archives are quotes; that

is, text strings in any language cited from the source from which location, time, and event are extracted.

Every single data point in the set can thereby have a different location and a variety of parameters describing

the event (Glaser, 1996). The time step can be, but is not necessarily, periodic. The quote might contain infor-

mation regarding the temperature in a city, precipitation conditions, and the resulting water level in a river,

as well as statements concerning harvest amount and quality of a certain crop. The resulting data type can be

boolean (for presence/absence), integer (for indices), real numbers with units for measurements, or enu-

merations (Riemann et al., 2016).

he documentary archives WG identiﬁed nine properties, which concerned the source material, including ori-

ginal scans of the documents, quote ID, language, and reference to the source material (e.g., DOI, license,

and page). Among these nine archive‐speciﬁc properties, four (the quote, reference to the quote, the quote

ID, and the quote's DOI) were voted as essential and ﬁve as recommended for new data sets. For legacy data

sets, only two (the quote and its reference) were identiﬁed as essential (Figures 5, 6, and 13). Four survey

respondents indicated that they were least familiar with this type of archive, which may help explain why

fewer properties compared to other archives were considered essential for optimal reuse of the resource by

researchers not familiar with the intrinsic details of the archive.

4.3. Uncertainties

The uncertainties WG identiﬁed seven properties applicable to most records. These properties fell into two

broad categories concerning the uncertainty in the measured variable (analytical uncertainty, number of

repeat measurements, and reproducibility) and the uncertainty associated with models to infer variables,

including chronologies (output statistics, output ensembles along with the parameters, and the publication

in which the model is described). For new data sets, four properties (analytical uncertainty, number of repeat

measurements, the publication, and parameters of the model) were deemed essential and the other three

recommended. For legacy data sets, only one was deemed essential (number of repeat measurements),

Figure 13. Mind map of the various properties identiﬁed by the documentary archives WG and associated vote. Color is

the same as in Figure 5. Parentheses indicate recommendations for legacy data sets when different from new data sets.

Available online at https://coggle.it/diagram/W4XNNeGhIngfjHzB/t/historical‐documents.

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while the rest were recommended. This highlights the commitment of the community to better characterize

uncertainties in paleoclimate records and the acknowledgement that uncertainty has often been

ignored when reporting data sets in the past, making it difﬁcult to include metadata for legacy data sets

(Figures 5, 6, and 14).

Respondents voted on reporting the analytical uncertainty and reproducibility as 2‐sigma (estimated as the

standard error of the mean), although a point was raised that the reporting should be community‐speciﬁc,

following their own accepted standards (e.g., radiocarbon; Stuiver & Polach, 1977, Millard, 2014), but clearly

indicated in the metadata. A compromise is to keep community‐speciﬁc standards while encouraging 2‐

sigma reporting if there is no preexisting standard.

For models, the method used should be documented both in the papers and with the data, with publication

information about the software and parameters used being considered essential for new data sets. For legacy

data sets, all information about the model is considered recommended.

The uncertainties WG has barely scratched the surface of uncertainty reporting in paleoclimate

studies. Although several other WGs have reported that uncertainty should be an essential parameter,

there is not yet a clear path forward as to how this uncertainty should be unambiguously reported.

However, there is some consensus that the method of reporting does not matter as long as the method

is clearly described. To do so, the LinkedEarth Ontology (Emile‐Geay et al., 2019) offers several paths

forward. The class Uncertainty can refer to a single value for all the data values, to a list of values of

equal length as the uncertain variable, and to models output stored in ensemble, summary, and

distribution tables.

Consider the example of radiocarbon dating. Each radiocarbon value is associated with an uncertainty that is

often reported in a separate column of the measurement table. This radiocarbon ‐age uncertainty is then

translated (via a calibration curve) into a calendar age uncertainty that is also stored in a separate column.

In both of these cases, the uncertainty is a variable that can be described with the same richness as other col-

umns in the data table. Furthermore, probabilistic age modeling software such as Bchron (Haslett & Parnell,

2008) and BACON (Blaauw & Christen, 2011) for radiocarbon, HMM‐Match (Lin et al., 2014) for strati-

graphic alignments, and the Banded Age Model (Comboul et al., 2014) return possible age distributions

around the calendar age value and age model ensembles for each depth in the paleorecord. In this particular

example, each measured value has at least one associated uncertainty value, possibly an entire

probability distribution.

On the other hand, uncertainty associated with measurements of trace elements and stable isotopes is often

reported as the uncertainty of the standard or a handful of replicates that are taken to represent the uncer-

tainty for all values. The LinkedEarth Ontology (Emile‐Geay et al., 2019) allows for the speciﬁcation of

not only the values and units of the uncertainty but also how this uncertainty is estimated and the level at

which it is being reported (e.g., one standard error of the mean).

Figure 14. Mind map of the various properties identiﬁed by the uncertainties WG and associated vote. Color is the same as in Figure 5. Parentheses indicate recom-

mendations for legacy data sets when different from new data sets. Available online at https://coggle.it/diagram/W4gttodcxjfvSst0/t/uncertainties.

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4.4. Chronologies

The chronologies WG identiﬁed 54 properties, 43 of which were deemed essential for new data sets, 10

recommended, and 1 desired. For legacy data sets, 30 were identiﬁed as essential, 22 as recommended,

and 2 as desired (Figures 5, 6, and 15).

Chronologies are obtained using two methods: absolute and relative. Relative chronologies often involve the

alignment of one paleoclimate time series with another of known age. For instance, benthic foraminifera

stable oxygen isotope (δ

O) records have often been aligned to the dated LR04 benthic δ

O stack

(Lisiecki & Raymo, 2005). For this type of chronology, the original measurements (e.g., benthic foraminifera

O), the alignment target (e.g., LR04 benthic δ

O stack), its associated reference chronology (e.g., LR04

age model), and alignment method (e.g., HMM‐Match; Lin et al., 2014) should be clearly identiﬁed (essen-

tial) for both new and legacy data sets. We acknowledge that there is potentially more work to be done to

devise a standard for relative chronologies, which should include an integration framework for biostratigra-

phy, paleomagnetism, stable isotopes chronologies, and orbitally tuned chronologies.

Absolute chronologies are based on radiometric measurements (commonly radiocarbon, lead, and uranium‐

decay series, or terrestrial cosmogenic nuclide), layer counting, counting of annual cycles in geochemical/

isotopic proxies, dendrochronological or tephrochronological crossdating, or luminescence. In addition,

some records are characterized by ﬂoating chronologies that are absolutely dated (within the uncertainty

of the radiometrically derived age), but which have a precise internal chronology due to clear annual

banding/cycles (e.g., U‐series dated fossil corals and radiocarbon‐dated tree chronologies).

Figure 15. Mind map of the various properties identiﬁed by the chronologies WG and associated vote. Color is the same as in Figure 5. Parentheses indicate recom-

mendations for legacy data sets when different from new data sets. Available online at https://coggle.it/diagram/W4hzXeGhIi5Fm0q7/t/chronologies.

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The radiocarbon community has a long history of standardizing the reporting of their measurements. In

1977, Stuiver and Polach highlighted recommendations that have remained mostly unchanged (Stuiver &

Polach, 1977). For chronological studies using the Libby half‐life (Libby et al., 1949), Stuiver and Polach

recommend reporting the δ

C ratio, the conventional radiocarbon age (relative to CE 1950), associated error

(expressed as ± one standard deviation), the estimated reservoir correction, and (optionally) the per mil

depletion or enrichment with respect to 0.95 NBS Oxalic acid standard (Olsson, 1970). For geochemical sam-

ples, dendrochronological samples, reservoir equilibria, and diffusion models, they recommend reporting

the δ

C ratio, percent modern, and δ

C and Δ

C based on the Cambridge half‐life of 5730 years

(Godwin, 1962). These guidelines were further extended to include postbomb

C data (Reimer et al.,

2004) and the reporting of calibrated dates (Millard, 2014) and formed the basis of the properties that were

put to a vote. Given the long history of standardization, it is not surprising that legacy radiocarbon data sets

are also held at a stringent reporting level.

For U‐Th dating, the WG recommended the use of the standard proposed by Dutton et al. (2017), with most

properties recognized as essential when reporting U‐series dates.

Survey respondents also deﬁned what information should be included when reporting the use of age model-

ing software. The method's name is deemed essential for both legacy and new data sets with most of the other

properties identiﬁed as recommended. In addition, there is interest in storing ensembles of posterior draws

from Bayesian approaches to ensure that the study is fully reproducible. The LiPD structure is already setup

to handle multiple model output instances, allowing updates of chronologies for legacy data sets when raw

data are available. They thus provide a natural container to store this information.

Finally, respondents were asked to de ﬁne some nomenclature, including the use of present in paleoclimate

studies. Over 80% of respondents voted on keeping the concepts of age and year separated. Age is represented

on a time axis starting from the present and counting positively back in time. On the other hand, year follows

the Gregorian calendar and is particularly useful for studies concentrating on the past 2,000 years. Over 60%

of respondents also voted on reporting years relative to CE (Common Era) rather than AD.

Asking for a deﬁnition of present yielded diverse results. Sixty‐eight percent of respondents voted in favor of

using 1950 as the present, following the radiocarbon convention, 7% voted in favor as deﬁning present as the

last year in a record (with no mention of uncertainty), 12% voted in favor of using 2000 as the present, while

the last 13% answered other. This last category includes the use of 1950 for radiocarbon and either something

else for the other chronologies or readjusting to 1950 to stay in tune with radiocarbon and the use of either

1950 or 2000 as long as it is clearly deﬁned with the data. In summary, there is a consensus that present

should be deﬁned as an absolute date (and reported in the metadata), but it should be archive‐dependent,

with practitioners of U‐series dating leaning towards CE 2000 and practitioners of radiocarbon dating lean-

ing towards CE 1950.

One issue in reporting ages is, again, the lack of standards. The most common standard for time and date

reporting (e.g., ISO 8601) does not accommodate for geologic time. The more recent OWL time ontology

draws on the work of Cox and Richards (2015) and includes these concepts. However, these authors offer

no ﬁner division of geologic time than eras. This means that the vast majority of archived paleoclimate data

sets (particularly, the totality of data sets archived on the LinkedEarth platform) would represent a single

time point (the Quaternary era). To remedy this gap between ISO 8601 and the OWL time representation,

we hereby propose a precise mechanism to report the time axis in paleoclimate data sets:

Time ageðÞ¼significand:10

exponent

years direction datum

where signiﬁcand and exponent are components of standard ﬂoating‐point representation; direction

indicates whether time ﬂows forward (since a datum, as in the case of AD dates), or backward (before a par-

ticular datum, as in the case of ages). Datum here refers to the origin point of the time (age) axis, which is

arbitrary and (as recounted by Wolff, 2007) highly inconsistent among researchers.

Table 1 shows how this representation would work in practice. Note that variability in the datum for rows 1

(21 ky BP, a common date for the Last Glacial Maximum) and 4 (127 ky BP, a common date for Marine

Isotope Stage 5e) could arise because of the date being reported from a radiocarbon versus U‐series chronol-

ogy and is usually impossible to infer without clariﬁcation from the original publication, or from its authors.

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The current proposal removes such ambiguities and can accommodate both observed and simulated data

sets, potentially easing the task of model‐data comparison if both communities start adopting it.

5. An Example: MD98‐2181

This section puts these recommendations into practice on a real‐world data set: the MD98‐2181 marine sedi-

mentary record from Khider et al. (2014). The purpose is twofold: (1) illustrate how to implement these

recommendations in practice and (2) draw attention to practical difﬁculties that may impede large‐scale

adoption of PaCTS v1.0.

MD98‐2181 is the most metadata‐rich data set currently available on the LinkedEarth platform since it was

used as an example to further develop the LiPD framework and later the LinkedEarth Ontology. The data set

consists of measurements of Mg/Ca and δ

O made on the planktic foraminifera Globigerinoides ruber

(white, sensu stricto, and sensu lato) and δ

O made on the benthic foraminifera Cibicidoides mundulus to

infer surface and deep ocean variability in the western tropical Paciﬁc over the Holocene. The age model

is based on radiocarbon measurements for the Holocene and deglacial portion of the core.

Using the standards proposed for cross‐archive metadata, Mg/Ca and δ

O on foraminifera, radiocarbon‐

based chronology, and uncertainties, we calculated how many metadata properties in the essential and

recommended categories were present in the MD98‐2181 data sets (Figure 16). Since, by default, all metadata

are desired, we ignored this category for the purpose of this example. In terms of its cross‐archive metadata,

the MD98‐2181 record is nearly complete, with 95% of the essential metadata and 78% of the recommended

metadata present in the record (Figure 16). The only missing component of essential metadata is the sample

thickness. For the recommended category, the International Geo Sample Number for the sample and date at

which the measurements were performed (i.e., analysis date) are missing. The core International Geo Sample

Number should be assigned by the core repository directly (e.g., Bremen Core Repository and Oregon State

University core repository). Both analysis dates and sample thickness are metadata readily available at the

time of collection. Although both were collected in either a physical notebook or by the instrument during

Table 1

Illustration of Our Proposed Time Representation With Four Time Points

Reported Age/year in manuscript Signiﬁcand Exponent Direction Datum

21 ka BP 21 3 before 1950 CE

1816 AD 1816 0 since 0 CE

2.7 Ma 2.7 6 before 1950 CE

127 ka BP 127 3 before 2000 CE

Note. The ﬁrst column gives examples of reported age/year in a paleoclimate paper, while the last four columns show an implementation of the representation

proposed here.

Figure 16. Radar plot showing the completeness of the metadata reporting for core MD98‐2181 (Khider et al., 2014) for

properties considered (a) essential and (b) recommended in the current study. The axis refers to the working group

standards recommendation applicable to the record.

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analysis, they were not archived with the data set on LinkedEarth since the information was not deemed by

the metadata authors as essential for reproducibility.

The paleodata for the record consist of Mg/Ca and δ

O measurements on foraminifera tests from sediment

core subsamples. For the essential reporting of δ

O on foraminifera, the MD98‐2181 record lacks metadata

regarding the taxonomy scheme being followed and equilibrium offsets. In the recommended category, only

the volume of sediment analyzed is missing. For Mg/Ca reporting, the contamination indicator values (Mn/

Ca and Fe/Ca; Khider et al., 2014) are missing from the archived record in addition to the taxonomy scheme

being followed. Neither were deemed useful for reproducibility by the authors of the study at the time of

reporting. In the recommended category, the volume of sediment analyzed and habitat depth has not been

reported. In both cases, the values are unknown, either because they were not measured during sample pre-

paration (sediment analyzed) or could not be accurately determined (habitat depth) from previous studies in

the region.

The MD98‐2181 chronology was based on radiocarbon measurements. Ninety percent of the raw radiocar-

bon dates used in Khider et al. (2014) were reported in Stott et al. (2004, 2007). The raw data necessary for

the repeatability and replicability of the age model in Khider et al. (2014) were rereported in the later study.

However, the archived record is missing information about the modern fraction (F14C), the sample ID, and

the matrix, which are deemed essential. The archived record is also missing most of the recommended prop-

erties, only reporting the reservoir age correction (ΔR), the ensemble statistics, and the ensemble age models.

The last two properties are essential in the context of the Khider et al. (2014) study to reproduce the age‐

uncertain spectral analysis. The Stott et al. (2004, 2007) studies are also missing the essential and recom-

mended properties with respect to reporting of raw measurements.

For uncertainty quantiﬁcation, the record metadata lack the number of repeated measurements and the

model parameters in the essential category, though it should be noted that the values of repeated measure-

ments are reported in the measurement table itself. The record is complete in the recommended category.

This example highlights the difﬁculty of reporting all essential metadata, especially after the study has been

completed. We therefore present version 1.0 of PaCTS as an aspirational standard, one that would theoreti-

cally ensure optimal reuse of paleoclimate data sets but is difﬁcult to observe in practice. Clearly, being aware

of these requirements at the start of a study would help scientists keep track of the necessary metadata and

ensure that they are reported when the data set is digitally published (e.g., on WDS‐Paleo or PANGAEA). We

therefore recommend that investigators plan ahead of time which properties they intend to report and struc-

ture their lab notebooks so this information is easier to track at the time of publication.

6. Discussion

This paper describes the ﬁrst effort by the global paleoclimate community to deﬁne standards for digitally

archiving paleoclimate data sets. Such standards aim to make publicly archived paleoclimate data more reu-

sable by clearly describing them with comprehensive metadata. In combination with the LinkedEarth

Ontology, these standards also help meet the interoperability principle by using a formal, accessible, shared,

and broadly applicable language for knowledge representation. If the data sets are properly described using

microdata (e.g., Schema.org), they are also ﬁndable. Together, these standards bring such data sets closer to

compliance with FAIR principles.

The standards arose through collective discussions, both in person and online, and via an innovative social

platform (Gil et al., 2017). The results of this collective decision‐making reveal an evident desire for archiving

a rich set of metadata properties, with respondents identifying roughly two thirds of properties (208 out of

302) as essential for new data sets. Respondents also recognized that legacy data sets may not be as complete,

so they identiﬁed less stringent requirements in order not to overlook valuable data sets. Nonetheless,

respondents identiﬁed 131 properties as essential for legacy data sets, highlighting the fact that a data set

loses its usefulness if too many requirements are not met. Several respondents also indicated that while some

properties should theoretically be essential (or recommended), they may be hard to obtain in practice and/or

variable in time. These include seasonality and habitat depth of foraminifera and many of the properties

from TRiDaS. Furthermore, although rich metadata are always valuable, these requirements should be

balanced with the researcher's time. Scans of historical documents or uploads of X‐radiographs of archive

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samples would be highly valuable to the community, but these activities are time‐consuming and this use of

time is rarely, if ever, incentivized by funding agencies.

PaCTS v1.0 is also missing several proxy systems, including loess and continental records, faunal and ﬂoral

counts in lake sediments, and does not incorporate recent standards such as the one developed by Courtney

Mustaphi et al. (2019) for

210

Pb dating. Finally, although cross pollination was encouraged, common proper-

ties were not adequately identiﬁed across WGs, resulting in duplicates. This is especially apparent in the lake

and marine sediment WGs.

Another salient outcome is that this ﬁrst version of PaCTS can only be described as aspirational. Indeed,

section 5 illustrates that even in the best of circumstances (the author describing their own data set, gener-

ated less than a decade ago), the compliance rate was far from perfect. This points to the need for more rea-

listic guidelines. It is indeed apparent that many participants misinterpreted what was meant by essential.

Further, the participation rate is still far below what is needed for this standard to be representative of the

worldwide paleoclimate community, which would gain much from harmonization. How can this standard

be collectively reﬁned and more broadly adopted? How should the standard, and its future versions, be

implemented in practice?

6.1. Broadening Participation

The genesis of PacTS v1.0 serves as a useful template for future efforts. As detailed in section 2, the spark for

the discussion came from the 2016 workshop on Paleo Data Standards. Nothing replaces the immediacy of

in‐person communication for this sort of work. However, it would be costly, carbon‐intensive, and unrealis-

tic to expect large segments of the paleoclimate community to travel for such an event, should it happen

again. We therefore advocate that further discussion takes place within, or around, existing meetings.

Examples include the annual meetings of the American Geophysical Union and the European

Geosciences Union, the Goldschmidt conference, Ocean Sciences meeting, the PAGES Open Science

Meeting, the International Conference on Paleoceanography, meetings of the International Union for

Quaternary Research, and more focused meetings like WorldDendro, Karst Record, or the ASLO Aquatic

Sciences Meeting. We have also found PAGES‐sponsored workshops to be excellent opportunities to discuss

data stewardship considerations, of which reporting standards are an important aspect. At the very least, an

annual session at an international meeting would be useful for the community to touch base and take stock

of progress and challenges, but more frequent interactions will be desirable until adoption reaches a critical

threshold (e.g., 80% of submissions to public repositories like WDS‐Paleo or PANGAEA).

Assuming that such meetings will take place over the next few years in many corners of the community,

there is still a need for more sustained forms of communication. The virtual working groups on the

LinkedEarth platform are where many of our discussions took place, and they remain available to comple-

ment the in‐person discussions. Membership is open, and we encourage interested readers to join

LinkedEarth so they can participate in these forums or create their own forums on a platform of their choice

(traceability and transparency being of paramount importance).

6.2. Roadmap to Standardization

In practical terms, we recommend that the next iteration of PaCTS use the following steps:

1. The procedure for ratiﬁcation is developed in tandem with major stakeholders (scientiﬁc societies, data

repositories, and chief editors).

2. The proposed procedure is widely distributed to the community (e.g., through the PAGES magazine,

AGU and EGU communication channels, and social media).

3. The timeline for discussion and voting is clearly indicated, and voting occurs on the LinkedEarth

platform.

4. The vote outcome is presented at a major international meeting, and any additional discussion is consid-

ered before the vote is certiﬁed at the meeting.

5. The standard is widely disseminated and encouraged by appropriate incentives (see below).

6.3. Implementing Emerging Standards

We envision two main ways to encourage the adoption of the standard. The ﬁrst is to use technical innova-

tion to lower the barrier to metadata archiving; the second is to change the incentive structure to make it

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worthwhile for researchers to adopt the standard, despite the inevitable opportunity cost that comes with

providing more complete data records.

On the ﬁrst point, the LinkedEarth project has recently implemented a web interface to convert paleoclimate

data sets into the LiPD format: the lipd.net playground (http://lipd.net/playground). To promote standardi-

zation, the reporting recommendations described herein will be ﬂagged as users create LiPD ﬁles interac-

tively on the lipd.net website, pulling data and metadata from native archival formats (e.g., Excel

spreadsheets). Ideally, all records, especially those accepted on the LinkedEarth platform, will show their

compliance rate with PaCTS. This rate can be computed during creation of the LiPD ﬁle, allowing unavail-

able as an answer for the essential ﬁelds. At present, the lipd.net playground displays the rate of required

ﬁelds that have been entered but is not set up to track archive or proxy‐speciﬁc completeness, although this

is possible with further development. The unavailable category serves two purposes: (1) to encourage

researchers to gather these metadata during their next study and (2) to investigate how many of these essen-

tial properties are reported in practice. Alternatively, LinkedEarth could appoint a Board of Data Editors to

approve the data sets for upload onto the platform. The Board presents several advantages over an automatic

process: (1) to answer speciﬁc questions, therefore taking into consideration the intricacies of a data set; (2) to

identify needed changes to the reporting standards faster; and (3) to assist the community with the online

Web service when needed. The major drawback is the volunteer time of the Board of Data Editors. In our

experience, the time of researchers is already stretched thin, and they have little incentive to commit more

of it to the relatively thankless task of standardization.

How might the reward structure be changed? There are essentially two levers to activate. The ﬁrst is funding

agencies. In the United States, for instance, the National Science Foundation funds the vast majority of

paleoclimate research. While the agency now requires a data management plan to be submitted for each pro-

posal, its reporting guidelines are very broad. They could be made more speciﬁc and point paleoclimate

researchers to the latest version of PaCTS. The European Research Council similarly supports Open

Science, but with far less speciﬁc guidelines than PaCTS v1.0. To the best of our knowledge, the situation

is similar for other countries (e.g., Canada and Australia). We therefore call on funding agencies to either

endorse this standard or propose a meaningful alternative.

The second lever is publishers and editors: while each publishing house encourages digital data archiving to

varying degrees, the decision of what (meta)data to include is ultimately up to the author and often fails to

consider the long‐term value proposition of the data set. Publishers could help ensure that the present stan-

dard is, at the very least, encouraged, if not mandatory. In particular, the American Geophysical Union and

Copernicus publishers recently endorsed requirements to make data FAIR. Afﬁliated journals could use

their leverage to promote more stringent reporting standards. As an example, the recent PAGES 2k special

issue of the journal Climate of the Past piloted the implementation of open‐data practices, which included

some reporting standards, and reported the challenges faced when requiring such practices (Kaufman

et al., 2018). Another avenue for promoting best practices, including adoption of reporting standards, is

through professional paleoscience organizations such as PAGES and INQUA.

We expect the present reporting standard to evolve to meet the needs of the paleoclimate community. It is

our hope that this publication will stimulate volunteers to join the effort and organize discussions at all com-

munity levels; there can be no community standard without community involvement. We are conﬁdent that

improving paleoclimate data standards will promote collaboration on international data syntheses and

encourage the development of software based on the new standards. In turn, such software will reduce the

time to science, by compressing the time researchers spend on the menial task of data wrangling.

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Erratum

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In the originally published version of this paper, author J.J. Williams was erroneously omitted from the

author list. Also, there was an error in the afﬁliations that erroneously listed Richard Telford's institution

in Germany, instead of Norway. These errors have since been corrected, and this version may be considered

the authoritative version of record.